Recognition of High-Range-Resolution (HRR) Profile Signatures of Moving Ground Targets for Combat Identification (CID)

Award Information
Agency:
Department of Defense
Branch
Navy
Amount:
$148,820.00
Award Year:
2007
Program:
SBIR
Phase:
Phase I
Contract:
N68936-07-C-0035
Award Id:
82507
Agency Tracking Number:
N071-017-0978
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
4725 B EISENHOWER AVENUE, ALEXANDRIA, VA, 22304
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
807454640
Principal Investigator:
Elvis Dieguez
Principle Investigator
(703) 212-8870
elvis.dieguez@mtsi-va.com
Business Contact:
David Kang
Director of Strategic Initiatives
(703) 212-8870
david.s.kang@mtsi-va.com
Research Institution:
n/a
Abstract
The objective of this proposal is to demonstrate the advantages of using a Hierarchical Hidden Markov Model for Aided Target Recognition of High Range Resolution (HRR) radar. A Hidden Markov Model (HMM) based technique has been previously shown to provide aided recognition of HRR with high probability of correct identification and low probability of error. This proposal extends current HMM techniques by utilizing a generalized HMM, known as the Hierarchical Hidden Markov Model, with several attractive properties not found in classic HMMs - in particular superior ability to learn the different stochastic levels and length scales present in the structure of the target features. One key difficulty in the application of any HMM is parameter estimation. The unknown parameters are typically point-estimated in a Maximum A Posterior (MAP) or Maximum Likelihood (ML) sense using an Expectation Maximization algorithm. We propose to utilize a Variational Bayes (VB) algorithm that does not generate a point estimate for the parameters but an approximation to the full posterior of the model parameters. The VB technique has shown in many applications to be less sensitive to overfitting and better-suited for active learning; the VB solution also allows one to perform model selection, here concerning the appropriate number of HMM states.

* information listed above is at the time of submission.

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